• CANDIDATE: A tool for generating anonymous participant-linking IDs in multi-session studies 

      Sandnes, Frode Eika (PLOS ONE;16 (12): e0260569, Peer reviewed; Journal article, 2021-12-15)
      Background: To ensure the privacy of participants is an ethical and legal obligation for researchers. Yet, achieving anonymity can be technically difficult. When observing participants over time one needs mechanisms to ...
    • SinGAN-Seg: Synthetic training data generation for medical image segmentation 

      Thambawita, Vajira L B; Salehi, Pegah; Sheshkal, Sajad Amouei; Hicks, Steven; Hammer, Hugo Lewi; Parasa, Sravanthi; de Lange, Thomas; Halvorsen, Pål; Riegler, Michael (PLOS ONE;17(5): e0267976, Peer reviewed; Journal article, 2022-05-02)
      Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a ...